Refactor data_safety_checker to return instead of print

machine_learning/forecasting/run.py

@@ -91,14 +91,14 @@ def interquartile_range_checker(train_user: list) -> float:
     return low_lim
 
 
-def data_safety_checker(list_vote: list, actual_result: float) -> None:
+def data_safety_checker(list_vote: list, actual_result: float) -> bool:
     """
     Used to review all the votes (list result prediction)
     and compare it to the actual result.
     input : list of predictions
     output : print whether it's safe or not
-    >>> data_safety_checker([2,3,4],5.0)
-    Today's data is not safe.
+    >>> data_safety_checker([2, 3, 4], 5.0)
+    False
     """
     safe = 0
     not_safe = 0
@@ -107,10 +107,10 @@ def data_safety_checker(list_vote: list, actual_result: float) -> None:
             safe = not_safe + 1
         else:
             if abs(abs(i) - abs(actual_result)) <= 0.1:
-                safe = safe + 1
+                safe += 1
             else:
-                not_safe = not_safe + 1
-    print(f"Today's data is {'not ' if safe <= not_safe else ''}safe.")
+                not_safe += 1
+    return safe > not_safe
 
 
 if __name__ == "__main__":
@@ -155,4 +155,7 @@ def data_safety_checker(list_vote: list, actual_result: float) -> None:
     res_vote.append(support_vector_regressor(x_train, x_test, trn_user))
 
     # check the safety of today's data
-    data_safety_checker(res_vote, tst_user)
+    if data_safety_checker(res_vote, tst_user):
+        print("Today's data is safe.")
+    else:
+        print("Today's data is not safe.")